EmptyDroplets (FDR <= 0.1) + scDblFindersetwd("/media/jacopo/Elements/re_align/MM/PRJNA723584/SAMN18822747/SRR14295364/")
# Load the libraries (from Sarah script + biomart)
library(tidyverse) # packages for data wrangling, visualization etc
library(Seurat) # scRNA-Seq analysis package
library(clustree) # plot of clustering tree
library(ggsignif) # Enrich your 'ggplots' with group-wise comparisons
library(clusterProfiler) #The package implements methods to analyze and visualize functional profiles of gene and gene clusters.
library(org.Hs.eg.db) # Human annotation package neede for clusterProfiler
library(ggrepel) # extra geoms for ggplo2
library(patchwork) #multiplots
library(reticulate)
Load and do the QC for the cellranger data
#list.files(".")
dat <- Read10X(data.dir ="./out/counts_filtered/")
dat <- CreateSeuratObject(dat) # Create the seurat object from the 10x data
kb.initial <- dat@assays[["RNA"]]@counts@Dim[[2]]
cat("Initial number of cells:", kb.initial,
"\nNumber of genes:", dat@assays[["RNA"]]@counts@Dim[[1]])
## Initial number of cells: 17512
## Number of genes: 36601
Empty cells were already filtered, check for % mt RNA and death markers:
# first calculate the mitochondrial percentage for each cell
dat$percent_mt <- PercentageFeatureSet(dat, pattern="^MT.")
# make violin plots
mt_rna = 20
max_counts = 40000
# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
VlnPlot(dat, features = c("nCount_RNA", "nFeature_RNA", "percent_mt")) + geom_hline(yintercept=mt_rna, linetype = "dotted")
plot1 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "percent_mt")
plot1 <- plot1 + geom_hline(yintercept=mt_rna, linetype = "dotted")
plot2 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2 <- plot2 + geom_vline(xintercept = max_counts, linetype = "dotted")
plot1
plot2
## cells retained by mt RNA content ( 20 %): 11690
## percentage of retained cells: 66.75 %
## cells retained by counts ( 40000 ): 11660
## percentage of retained cells: 66.58 %
Check the distribution of the cells with low counts and control death markers:
min_counts = 300
hist(dat@meta.data$nCount_RNA, breaks = 100, xlab = "Counts")
hist(dat@meta.data$nCount_RNA, breaks = 1000, xlab = "Counts", xlim = c(0,5000))
hist(dat@meta.data$nCount_RNA, breaks = 10000, xlab = "Counts", xlim = c(0,1000))
abline(v=min_counts, col="red", lty = 3)
The evident peak of cells with < 200 counts could contain dying
cells.
# Subset the dataset to focus only on those cells with low counts
dat.lowcount <- subset(dat, subset = nCount_RNA < min_counts)
# Get the mean of the counts for each gene and sort them decreasing
meanCounts <- rowMeans(GetAssayData(object = dat.lowcount, slot = 'counts'))
meanCounts <- sort(meanCounts, decreasing = T)
# A boxplot can help to observe the distribution of the means
#boxplot(meanCounts)
# Print the most highly expressed genes
head(meanCounts, 30)
## IGKC RPLP1 IGHA1 RPL10 RPS28 JCHAIN EEF1A1
## 18.1895457 2.3978994 2.1729360 1.6587689 1.3561309 1.2667318 1.2318026
## MALAT1 RPS18 B2M RPL41 SSR4 RPS14 RPL32
## 1.1812408 0.8873962 0.8092330 0.7486566 0.7188569 0.7010259 0.6871031
## RPS4X TMSB4X RPS27 RPL3 MT-CO2 RPS19 RPL30
## 0.6331216 0.6311676 0.6211529 0.6008793 0.5862237 0.5857352 0.5708354
## RPL29 RPL18A RPL19 RPS27A RPS23 MT-CO3 RPL39
## 0.5703468 0.5657059 0.5212506 0.4868100 0.4777723 0.4670249 0.4658036
## RPL8 RPL18
## 0.4572545 0.4494382
## cells retained by counts ( 300 ): 7565
## percentage of retained cells: 43.2 %
dir.create("result")
saveRDS(dat, file = "./result/SAMN18822747_clean_QC.Rds")
#Normalize
dat <- NormalizeData(dat)
# Find the first 4000 variabe features
dat <- FindVariableFeatures(dat, selection.method = "vst", nfeatures = 4000)
Set mean expression to 0 and variance across 1 to avoid highly expressed genes drive the forwarding analyses. Since negative expression is meaningless, scaled data are useful only for UMAP and clustering
# scale data, the scaled data are saved in:
# dat[["RNA"]]@scale.data
all.genes <- rownames(dat)
dat <- ScaleData(dat, vars.to.regress = c("percent_mt","nCount_RNA"))
dat <- RunPCA(dat, features = VariableFeatures(object = dat), verbose = F, seed.use = 1)
print(dat[["pca"]], dims = 1:5, nfeatures = 5)
## PC_ 1
## Positive: MKI67, NUSAP1, CENPF, TOP2A, ASPM
## Negative: RPL10, RPL19, RPS14, RPL41, RPL32
## PC_ 2
## Positive: IGHA1, IGHV3-43, IFI6, LINC01725, PDK1
## Negative: MKI67, NUSAP1, CENPF, TOP2A, ASPM
## PC_ 3
## Positive: RPLP1, RPS18, IGHA1, RPS4X, RPS5
## Negative: TMSB4X, NEAT1, KLF2, CCPG1, HLA-B
## PC_ 4
## Positive: NEAT1, ZFP36L2, PCDH9, FCRL5, AHNAK
## Negative: PPIB, MYDGF, SEC11C, PRDX4, MANF
## PC_ 5
## Positive: S100A10, RASGRP2, CLIC1, TSPO, KLF2
## Negative: MZB1, PDK1, HDLBP, SEL1L, NME1
UMAP is a graph-based method of clustering. The first step in this process is to construct a KNN graph based on the euclidean distance in PCA space:
dat <- FindNeighbors(dat, dims = 1:20)
The graph now can be used as input for the function
runUMAP()
dat <- RunUMAP(dat, dims = 1:20, seed.use = 1)
DimPlot(dat, reduction = 'umap', seed = 1)
## QC metrics
## markers